نوع مقاله : مقاله پژوهشی

نویسندگان

گروه علوم و مهتدسی آب، دانشکده کشاورزی، دانشگاه ولیعصر(عج) رفسنجان، رفسنجان، ایران

چکیده

سابقه و هدف:
فعالیت ­های صنعتی و کشاورزی و در نتیجه عنصرهای سنگین و سمی حاصل از آن­ها به ­شدت کیفیت آب­ ها، سلامت عمومی و محیط زیست را تهدید می­ کنند. بنابراین تعیین مناطق تحت تأثیر عناصر سنگین و بررسی ریسک آلودگی و عدم قطعیت ­های مکانی آب ­های سطحی به­ عنوان موضوعی مهم و حساس مطرح است که کمتر به آن پرداخته شده است. هدف اصلی تحقیق حاضر، ترکیب روش ­های شبکه ­های بیزین و تکنیک­ های شبیه ­سازی متوالی گوسی به منظور ارزیابی خطر آلودگی فلزهای سنگین و عنصرهای سمی در آب­ های سطحی منطقه مس سرچشمه است.
مواد و روش ­ها:
در این مقاله، از 924 نمونه آب مربوط به 82 نقطه از سه منطقه متفاوت شامل زه ­آب ­های سطحی رودخانه شور، سدهای رسوبگیر و سایت اصلی معدن کاری منطقه مس سرچشمه و 9 عنصر سنگین استفاده و نقشه­ های پهنه ­بندی عدم قطعیت ریسک تهیه شد. اطلاعات براساس استاندارد سازمان حفاظت محیط زیست در دو کلاس خطر کم و زیاد طبقه ­بندی شدند. از تحلیل بیزین و الگوریتم ­های یادگیری بیزین برای تحلیل و بررسی ویژگی ­های همبستگی عناصر سنگین و استخراج وزن­ های بیزین استفاده شد. براساس ساختار به ­دست آمده از شبکه بیزین، عناصر کلیدی آلودگی منطقه مورد مطالعه انتخاب شدند. برای این ۳ عنصر، احتمال شرطی به هر نقطه اختصاص داده شد و سنجه ریسک بیزین (BRI) به ­عنوان نرخ خطی وزن ­دهی کلاس ­های ریسک، محاسبه شد. در نهایت مدل­ سازی زمین ­آماری و روش شبیه­ سازی متوالی گوسی (SGS) برای تولید نقشه­ های ریسک آلودگی بر مبنای سنجه BRI و نقشه انحراف استاندارد سنجه ریسک بیزین در آنالیز عدم قطعیت ریسک در شبیه­ سازی متوالی گوسی به­ کار برده شد.
نتایج و بحث:
براساس نتایج تحلیل بیزین سه عنصر روی، آهن و مولیبدن به­ عنوان ویژگی­ های اساسی و کلیدی در تعیین و پیش­ بینی ریسک آلودگی زون­ های مورد مطالعه تشخیص داده شدند که متعلق به ساختار اصلی شبکه بیزین با الگوریتم درختی تعیین حداکثر وزن (MWST) بودند. نتایج نشان دادند که بیشترین ریسک آلودگی در منطقه ­های سایت اصلی معدن کاری و در سد رسوب­گیر وجود دارد. براساس نتایج حاصله از مؤلفه­ های BRIZn، BRIMo و BRIFe، قسمت­هایی از مناطق جنوبی و شمالی واقع در زون شماره 1 (سایت اصلی معدن­ کاری) و بیشتر نقاط زون شماره 3 (سد رسوب­گیر) شامل مناطق غربی، مرکزی و جنوبی، ریسک زیاد آلودگی دارند که باید تمهیدهای لازم برای رفع مشکل آلودگی منبع­ های آب در این مناطق اندیشیده شود. نتایج در زون شماره 2 (زه­آب جاری در رودخانه شور) ریسک آلودگی کمی را نشان دادند. بنابر نتایج، به ­طور متوسط به­ ترتیب 19 و 22 درصد مساحت زون­ ها در کلاس­ های ریسک خطر آلودگی زیاد و کم قرار گرفتند. نقشه پهنه­ های حاصل از ریسک و غلظت فلزهای سنگین نشان ­دهنده انحراف معیار زیاد و تغییرات وسیع در محدوده مجتمع مس و سد رسوب­گیر و بیانگر عدم قطعیت مکانی زیاد ریسک توزیع آلودگی فلزهای سنگین در منبع­ های آب سطحی حوزه مورد مطالعه است. نتایج تحلیل عدم قطعیت، انتقال فلزها از محل مجتمع مس و تجمع آن­ها در سد را نشان می ­دهد و نیاز به پایش و تصفیه فلزهای سنگین از زه ­آب تولیدی شرکت مس و لزوم دست­یابی بهتر فلزهای جانبی از زه­آب مس را ایجاب می ­نماید.
نتیجه­ گیری:
بنابر نتایج، آلودگی عنصرهای سنگین و سمی در منبع­ های آب منطقه مس سرچشمه و جریان­ های پایین دست آن بالاست که سبب نفوذ آلاینده­ ها به منبع­ های آب زیرزمینی دشت رفسنجان می­ شود. این وضعیت نشان دهنده فقدان تصفیه مناسب فلزهای سنگین در فرآیندهای مجتمع مس سرچشمه است.

کلیدواژه‌ها

عنوان مقاله [English]

Zoning and uncertainty analysis of heavy metal pollution risk in surface water resources of copper mine by Bayesian analysis and sequential Gaussian simulation

نویسندگان [English]

  • Akram Seifi
  • Hossien Riahi

Water Science and Engineering Department, Faculty of Agricultural, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

چکیده [English]

Introduction:
Industrial and agricultural activities resulting in the production of toxic heavy metals may endanger water quality, public health, and the environment. Therefore, the determination of areas that are affected by heavy metals and spatial uncertainty of pollution risks are considered as an important and sensitive issue, which are less studied. The main aim of this study was to combine Bayesian network analysis with Sequential Gaussian Simulations (SGS) to evaluate the pollution risk of heavy metal and toxic elements in the surface water of Sarcheshmeh copper mine.
Material and methods:
In this study, a dataset of 924 water samples from 82 locations from three different zones including the surface water of Shour River, tailing dam, and also the main mining site of Sharcheshmeh copper complex and nine heavy metals were used. The information was classified into two risk classes of low and high according to the standard of the Department of Environment of Iran. A Bayesian analysis and learning algorithm were applied to investigate the characterization of heavy metal correlations and Bayesian weights extraction. Based on the obtained Bayesian network structure, important metals were chosen as key pollution parameters. For these metals, the conditional probability was dedicated to every observed point and then the Bayesian Risk Index (BRI) was calculated as a linear rating of the weighted risk classes. Finally, the geostatistical modeling and SGS were applied for generating pollution risk and standard deviation maps of BRI were used as an uncertainty measure of SGS based on BRI elements.
Results and discussion:
Based on the results of Bayesian analysis, three elements of Zn, Mo, and Fe were identified as the most important parameters of pollution risk in the studied zones, which were derived by the MWST Bayesian network. The highest risk existed in the main mining zone and sedimentation dam. The results of BRIzn, BRIMo, and BRIFe declared that areas in north and south of zone 1 and all of zone 2 had high pollution risk, which requires appropriate treatment operations. The results also showed that the high-risk cluster was mainly located within the main mining and tailing dam zones. Also, 19% and 22% of zones’ area was classified as high and low risk of water pollution, respectively. Zoning maps of risk and heavy metals showed that there are high standard deviation and great variation in copper complex and distilling dam. The results of the uncertainty risk assessment showed high concentrations of heavy metals in the surface water arose from the transportation of heavy metal from copper mine to distilling dam, which requires treatment operation on the output water of the factory. 
Conclusion:
Based on the results, the pollution of heavy metal and toxic elements in water resources near Sarcheshmeh copper mine and downstream water resources was high and this will increase the pollution risk of Rafsanjan aquifer. These indicate the inadequate treatment of heavy metals in Sarcheshmeh copper mine water.

کلیدواژه‌ها [English]

  • Geostatistics
  • Geographical information system
  • Sequential Gaussian simulations
  • Spatial uncertainty
  • Risk class
  • Sarcheshmeh copper mine
  • Variogram

Albuquerque, M.T.D., Gerassis, S., Sierra, C., Taboada, J., Martin, J.E., Antunes, I.M.H.R. and Gallego, J.R., 2017. Developing a new Bayesian risk index for risk evaluation of soil contamination. Science of the Total Environment. 603, 167-177.

Ali, A., Javed, S., Ullah, S., Fatima, S.H., Zaidi, F. and Khan, M.S., 2018. Bayesian spatial analysis and prediction of groundwater contamination in Jhelum City (Pakistan). Environmental Earth Sciences. 77(3), 87.

Anbari, M.J. and Tabesh, M., 2015. Failure event probability calculation in wastewater collection systems using the Bayesian network. Journal of Water and Wastewater. 27(3), 47-63. (In Persian).

Antunes, I.M.H.R. and Albuquerque, M.T.D., 2013. Using indicator kriging for the evaluation of arsenic potential contamination in an abandoned mining area (Portugal). Science of the Total Environment. 442, 545-552.

Bień, J.D., Ter Meer, J., Rulkens, W.H. and Rijnaarts, H.H.M., 2005. A GIS-based approach for the long-term prediction of human health risks at contaminated sites. Environmental Modeling and Assessment. 9(4), 221-226.

Chiou, R.J., 2008. Risk assessment and loading capacity of reclaimed wastewater to be reused for agricultural irrigation. Environmental Monitoring and Assessment. 142(1-3), 255-262.

Cocârţă, D., Stoian, M. and Karademir, A., 2017. Crude oil contaminated sites: evaluation by using risk assessment approach. Sustainability. 9(8), 1365.

Conrady, S. and Jouffe, L., 2015. Bayesian networks and BayesiaLab: A Practical Introduction for Researchers. Bayesia, USA.

Davies, A.J. and Hope, M.J., 2015. Bayesian inference-based environmental decision support systems for oil spill response strategy selection. Marine Pollution Bulletin. 96(1), 87-102.

Druzdzel, M.J. and Henrion, M., 1993. Intercausal reasoning with uninstantiated ancestor nodes. In Proceedings of 9th International Uncertainty in Artificial Intelligence Congress, 9th-11th July, Washington, DC, USA. p.317.

Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P. and Bengio, S., 2010. Why does unsupervised pre-training help deep learning?. Journal of Machine Learning Research. 11, 625-660.

Ersoy, A. and Yunsel, T.Y., 2019. Geochemical modelling and mapping of Cu and Fe anomalies in soil using combining sequential Gaussian co-simulation and local singularity analysis: a case study from Dedeyazı (Malatya) region, SE Turkey. Geochemistry: Exploration, Environment, Analysis. 19(4), 331-342.

Gerstenberger, M.C., Christophersen, A., Buxton, R. and Nicol, A., 2015. Bi-directional risk assessment in carbon capture and storage with Bayesian networks. International Journal of Greenhouse Gas Control. 35, 150-159.

Getis, A. and Ord, J.K., 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis. 24(3), 189-206.

Ghaderian, S.M. and Ravandi, A.A.G., 2012. Accumulation of copper and other heavy metals by plants growing on Sarcheshmeh copper mining area, Iran. Journal of Geochemical Exploration. 123, 25-32.

Ghorbani, M.A. and Dehghani, R., 2017. Comparison of Bayesian neural networks and artificial neural network to estimate suspended sediments in the rivers, case study: Simineh Rood. Environmental Science Technology. 19(2), 1-13. (In Persian).

Giri, S. and Singh, A.K., 2014. Assessment of surface water quality using heavy metal pollution index in Subarnarekha River, India. Water Quality, Exposure and Health. 5(4), 173-182.

Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, USA.

Graham, S.E., Chariton, A.A. and Landis, W.G., 2019. Using Bayesian networks to predict risk to estuary water quality and patterns of benthic environmental DNA in Queensland. Integrated Environmental Assessment and Management. 15(1), 93-111.

Hesar, A.S., Tabatabaee, H. and Jalali, M., 2012. Monthly rainfall forecasting using Bayesian belief networks. International Research Journal of Applied and Basic Sciences. 3(11), 2226-2231.

Huang, X., Sillanpää, M., Gjessing, E.T., Peräniemi, S. and Vogt, R.D., 2010. Environmental impact of mining activities on the surface water quality in Tibet: Gyama valley. Science of the Total Environment. 408(19), 4177-4184.

Huang, X., Sillanpää, M., Gjessing, E.T., Peräniemi, S. and Vogt, R.D., 2010. Environmental impact ofmining activities on the surfacewater quality in Tibet: Gyama valley. Science of the Total Environment. 408(19), 4177–4184.

Juang, K.W., Chen, Y.S. and Lee, D.Y., 2004. Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environmental Pollution. 127(2), 229-238.

Keshavarzi, B., Moore, F. and Sharifi, R., 2013. Evaluation of dispersion and chemical partitioning patterns of heavy metals in the Sar Cheshmeh porphyry copper deposit: geochemical data from mine waste, water and stream sediments. International Journal of Environmental Studies. 70(1), 73-93.

Khorasanipour, M. and Eslami, A., 2014. Hydrogeochemistry and contamination of trace elements in Cu-porphyry mine tailings: a case study from the Sarcheshmeh mine, SE Iran. Mine Water and the Environment. 33(4), 335-352.

Khorasanipour, M., Tangestani, M.H., Naseh, R. and Hajmohammadi, H., 2011. Hydrochemistry, mineralogy and chemical fractionation of mine and processing wastes associated with porphyry copper mines: a case study from the Sarcheshmeh mine, SE Iran. Applied Geochemistry. 26(5), 714-730.

Kuhnert, P.M. and Hayes, K.R., 2009. How believable is your BBN. In Proceedings of 18th World IMACS/MODSIM Congress, 13th-17th July, Cairns, Australia. p.4319.

Lahr, J. and Kooistra, L., 2010. Environmental risk mapping of pollutants: state of the art and communication aspects. Science of the Total Environment. 408(18), 3899-3907.

Lee, C.J. and Lee, K.J., 2006. Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal. Reliability Engineering & System Safety. 91(5), 515-532.

Lyu, Z., Chai, J., Xu, Z. and Qin, Y., 2018. Environmental impact assessment of mining activities on groundwater: case study of Copper Mine in Jiangxi Province, China. Journal of Hydrologic Engineering. 24(1), 1-9.

Malakooti, S.J., Shahhosseini, M., Ardejani, F.D., Tonkaboni, S.Z.S. and Noaparast, M., 2015. Hydrochemical characterisation of water quality in the Sarcheshmeh copper complex, SE Iran. Environmental Earth Sciences. 74(4), 3171-3190.

Marcot, B.G., 2012. Metrics for evaluating performance and uncertainty of Bayesian network models. Ecological Modelling. 230, 50-62.

McDonald, K.S., Ryder, D.S. and Tighe, M., 2015. Developing best-practice Bayesian belief networks in ecological risk assessments for freshwater and estuarine ecosystems: a quantitative review. Journal of Environmental Management. 154, 190-200.

Mehrabi, B., Mehrabani, S., Rafiei, B. and Yaghoubi, B., 2015. Assessment of metal contamination ingroundwater and soils in the Ahangaran mining district, west of Iran. Environmental Monitoring and Assessment. 187(727), 1-23.

Mimba, M.E., Ohba, T., Fils, S.C.N., Wirmvem, M.J., Numanami, N. and Aka, F.T., 2017. Seasonal hydrological inputs of major ions and trace metal composition in streams draining the mineralized Lom Basin, East Cameroon: basis for environmental studies. Earth Systems and Environment. 1(2), 22.

Qu, M., Li, W. and Zhang, C., 2014. Spatial distribution and uncertainty assessment of potential ecological risks of heavy metals in soil using sequential Gaussian simulation. Human and Ecological Risk Assessment: An International Journal. 20(3), 764-778.

Mohajerani, H., Mosaedi, A., Kholghi, M., Meftah Halaghi, M. and Saddodin, A., 2009. Bayesian decision networks introduction and their applications in water resources management. In Proceedings First National Coastal Lands Water Resources Management Congress, 17th-18th November, Sari Agricultural Sciences and Natural Resources University, Sari, Iran. p.11.

Nikoo, M. and Kerachian, R., 2009. Evaluating the efficiency of Bayesian networks in river quality management: application of the trading-ratio system. Water and Wastewater. 1(69), 23-33.

Nolan, B.T., Fienen, M.N. and Lorenz, D.L., 2015. A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA. Journal of Hydrology. 531, 902-911.

Pearl, J., 1986. Fusion, propagation, and structuring in belief networks. Artificial Intelligence. 29(3), 241-288.

Phan, T.D., Smart, J.C., Capon, S.J., Hadwen, W.L. and Sahin, O., 2016. Applications of Bayesian belief networks in water resource management: A systematic review. Environmental Modelling & Software. 85, 98-111.

Pollino, C.A., Woodberry, O., Nicholson, A., Korb, K. and Hart, B.T., 2007. Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling & Software. 22(8), 1140-1152.

Qiao, W., Li, W., Zhang, S. and Niu, Y., 2019. Effects of coal mining on the evolution of groundwater hydrogeochemistry. Hydrogeology Journal. 27(6), 2245-2262.

Qu, B., Zhang, Y., Kang, S. and Sillanpää, M., 2017. Water chemistry of the southern Tibetan Plateau: an assessment of the Yarlung Tsangpo River basin. Environmental Earth Sciences. 76(74), 1-12.

Rahman, M.M., Hagare, D. and Maheshwari, B., 2016. Bayesian Belief network analysis of soil salinity in a peri-urban agricultural field irrigated with recycled water. Agricultural Water Management. 176, 280-296.

Rakotondrabe, F., Ngoupayou, J.R.N., Mfonka, Z., Rasolomanana, E.H., Abolo, A.J.N. and Ako, A.A., 2018. Water quality assessment in the Betare-Oya gold mining area (East-Cameroon): multivariate statistical analysis approach. Science of the Total Environment. 610, 831-844.

Rocha, M.M. and Yamamoto, J.K., 2000. Comparison between kriging variance and interpolation variance as uncertainty measurements in the Capanema iron mine, State of Minas Gerais–Brazil. Natural Resources Research. 9, 223–235.

Roozbahani, A., Zahraie, B. and Tabesh, M., 2013. Integrated risk assessment of urban water supply systems from source to tap. Stochastic Environmental Research and Risk Assessment. 27(4), 923-944.

Sahoo, M.M., Patra, K.C., Swain, J.B. and Khatua, K.K., 2017. Evaluation of water quality with application of Bayes' rule and entropy weight method. European Journal of Environmental and Civil Engineering. 21(6), 730-752.

Seifi, A. and Riahi, M.H., 2017. Qualitative zoning of Shahr-e-Babak aquifer based on its corrosiveness, sedimentation, and applicability for agricultural, drinking, and pressure irrigation uses. Water and Wastewater. 28, 92-105. (In Persian).

Shannon, C.E., 1948. A mathematical theory of communication. Bell System Technical Journal. 27(3), 379-423.

Shariatpanahi, M., 1992. Quality principles and treatment of water and wastewater, Fifth ed. Academic Press Inc., Tehran University, Tehran, Iran. (In Persian).

Sharifi, R., Moore, F. and Keshavarzi, B., 2013. Geochemical behavior and speciation modeling of rare earth elements in acid drainages at Sarcheshmeh porphyry copper deposit, Kerman Province, Iran. Chemie der Erde-Geochemistry. 73(4), 509-517.

Szatmari, G. and Pasztor, L., 2019. Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms. Geoderma. 337, 1329-1340.

Tabatabaei, A. and Dashtizadeh, P., 2008. Introduction of Bayesian networks and their application in bridge maintenance. In Proceedings 14th National Civil Engineering Conference, 26th August, Semnan, Iran. p. 6. (In Persian).

Taheriyoun, M., Alavi, V. and Ahmadi, A., 2016. Risk analysis of wastewater reuse in agriculture using Baysian network. Civil and Enviromental Engineering. 48(1), 38-40. (In Persian).

Taheriyoun, M., Alavi, V. and Ahmadi, A., 2016. Risk analysis of wastewater reuse in agriculture using Baysian network. Amirkabir Journal of Civil Engineering. 48(1), 101-109.

Tiwari, A.K., Singh, P.K., Singh, A.K. and De Maio, M., 2016. Estimation of heavy metal contamination in groundwater and development of a heavy metal pollution index by using GIS technique. Bulletin of Environmental Contamination and Toxicology. 96(4), 508-515.

Wen, X., Feng, Q., Lu, J., Wu, J., Wu, M. and Guo, X., 2018. Risk assessment and source identification of coastal groundwater nitrate in northern China using dual nitrate isotopes combined with Bayesian mixing model. Human and Ecological Risk Assessment: An International Journal. 24(4), 1043-1057.

Wu, J., Xu, S., Zhou, R. and Qin, Y., 2016. Scenario analysis of mine water inrush hazard using Bayesian networks. Safety Science. 89, 231-239.

Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E. and Shepherd, K., 2016. A Bayesian network framework for project cost, benefit and risk analysis with an agricultural development case study. Expert Systems with Applications. 60, 141-155.

Yünsel, T.Y., 2019. In-situ coal quality variability analysis by combining Gaussian co-simulation and a JavaScript. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 41(21), 2631-2649.